Lightning AI Studios vs Ludwig

Lightning AI Studios is a cloud workspace for training models, running inference, building agents and AI apps, and coding collaboratively. Its persistent environments save installed packages, custom Python versions, and downloaded files. Studios offer browser-accessible VS Code and JupyterLab, plus AI copilots for AI and machine-learning work, including CUDA and PyTorch debugging. Teams can collaborate in real time on private projects, use a full-stack runtime, and deploy full-stack applications. Users can connect S3 buckets and EFS volumes through Lightning Drive, import models from Hugging Face and GitHub, or connect local IDEs such as VS Code, Cursor, and PyCharm over SSH. An SDK supports workflow automation. The Free plan is free and includes one active Studio, 50 GB persistent storage, and up to two concurrent GPUs; that Studio requires a restart every four hours. Pro and Teams plans have higher storage and GPU limits. Enterprise offers deployment in a customer VPC using AWS or GCP cloud credits.

Ludwig is an open-source deep learning framework for building, fine-tuning, and deploying models across data types including tabular, text, image, audio, time series, and geospatial data. Users describe preprocessing, model components, training, and hyperparameter optimization in a validated YAML configuration instead of writing training loops. It supports multimodal and multi-task models, plus LLM fine-tuning methods including SFT, DPO, KTO, ORPO, and GRPO, with parameter-efficient options such as LoRA and QLoRA. Training can scale through Ray using DDP, FSDP, or DeepSpeed; Kubernetes and KubeRay are also listed. Hyperparameter optimization integrates Ray Tune and Optuna, with SQLite or PostgreSQL persistence. Models can be served as REST APIs or exported to SafeTensors, ONNX, or torch.export. The framework also offers experiment tracking integrations, generated reports and visualizations, and explainability features. Ludwig is free under the Apache 2.0 License, supports Python, and runs on Linux, macOS, Windows, or self-hosted infrastructure. The FAQ notes that Unsloth may be faster for users focused only on LLM fine-tuning and maximum throughput.